GARDiff: Graph-Aligned Residual Diffusion for Probabilistic Multivariate Time-Series Forecasting
cs.LG, cs.AI
Submitted: 2026-09-29
Updated: 2026-09-29
Sources
- Diffusion-based Time Series Imputation and Forecasting with Structured State Space Models
- TimeFilter: Patch-Specific Spatial-Temporal Graph Filtration for Time Series Forecasting
- TimeBridge: Non-Stationarity Matters for Long-term Time Series Forecasting
- iTransformer: Inverted Transformers Are Effective for Time Series Forecasting
- TimesNet: Temporal 2D-Variation Modeling for General Time Series Analysis
- Non-stationary Diffusion For Probabilistic Time Series Forecasting
Related papers
- Polynomial-Augmented Neural Networks (PANNs) with Weak Orthogonality Constraints for Enhanced Function and PDE Approximation
- AIRL-S: Unifying Reinforcement Learning and Search-Based Test-Time Scaling via Adversarial Inverse Reinforcement Learning
- Transformers as Bayesian In-Context Experimenters: Smoothness-Adaptive Efficient ATE Estimation
- Convergence issues in Relational Concept Analysis based on AOC-posets
- Beliefs Beyond Posteriors: The Predictive Cost of ELBO Binding
- Understanding Diffusion Models via Ratio-Based Function Approximation with SignReLU Networks